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Probabilistic Estimation of Hidden Migrant Fatalities Along the Central Mediterranean Route

Gregor Zens, Zoe Sigman

Abstract

Estimating the number of migrants who die or go missing along dangerous routes such as the Central Mediterranean remains challenging as available records are incomplete. Some incidents are never documented, and fatalities associated with such unobserved incidents are absent from observed totals. We propose a Bayesian approach for probabilistic estimation of total migrant fatalities in such settings. Building on recent developments in multiple-systems estimation, we develop a time-stratified latent-class framework that accommodates missing fatality counts for unobserved incidents. We apply the method to recoded incident-level data from the Missing Migrants Project for the Central Mediterranean route from 2014 to 2025, encompassing 25,712 fatalities across 1,562 incidents. Our model yields 95% credible intervals of 30,426-39,172 fatalities and 2,200-2,591 deadly incidents, indicating that approximately 66%-85% of fatalities and 60%-71% of incidents are reflected in the available data. We estimate that unreported fatalities were concentrated between 2014 and 2016. Furthermore, we document that reporting likelihood increases with incident severity, implying that smaller incidents are most likely to remain undetected. While contingent on modeling assumptions and incomplete data, our method provides a broadly applicable and principled alternative to naive data adjustment methods.

Probabilistic Estimation of Hidden Migrant Fatalities Along the Central Mediterranean Route

Abstract

Estimating the number of migrants who die or go missing along dangerous routes such as the Central Mediterranean remains challenging as available records are incomplete. Some incidents are never documented, and fatalities associated with such unobserved incidents are absent from observed totals. We propose a Bayesian approach for probabilistic estimation of total migrant fatalities in such settings. Building on recent developments in multiple-systems estimation, we develop a time-stratified latent-class framework that accommodates missing fatality counts for unobserved incidents. We apply the method to recoded incident-level data from the Missing Migrants Project for the Central Mediterranean route from 2014 to 2025, encompassing 25,712 fatalities across 1,562 incidents. Our model yields 95% credible intervals of 30,426-39,172 fatalities and 2,200-2,591 deadly incidents, indicating that approximately 66%-85% of fatalities and 60%-71% of incidents are reflected in the available data. We estimate that unreported fatalities were concentrated between 2014 and 2016. Furthermore, we document that reporting likelihood increases with incident severity, implying that smaller incidents are most likely to remain undetected. While contingent on modeling assumptions and incomplete data, our method provides a broadly applicable and principled alternative to naive data adjustment methods.
Paper Structure (28 sections, 37 equations, 11 figures, 4 tables)

This paper contains 28 sections, 37 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Literature and Contributions
  3. Data and Scientific Questions
  4. List Creation
  5. Benefits and Limitations of the Meta-Observer Approach
  6. Research Questions
  7. Empirical Stylized Facts
  8. Statistical Methodology
  9. Stratified Mark-Augmented Latent Class Model
  10. Prior Elicitation
  11. Posterior Inference
  12. Results
  13. Summary, Discussion, and Concluding Remarks
  14. Further MCMC Details and Derivations
  15. Augmenting Missing Observations
  16. ...and 13 more sections

Figures (11)

  • Figure 1: Posterior estimates of migrant incidents and fatalities (2014--2025). Panel (a) shows the inferred total number of incidents. Panel (b) shows the inferred total number of fatalities.
  • Figure 2: Posterior estimates of total incidents (top) and total fatalities (bottom), stratified by year (left) and month (right). Shaded areas are 95% credible intervals. Solid black lines are posterior medians. Dashed black lines are reported totals.
  • Figure 3: Model-implied reporting probabilities. The left panel shows the relationship between the number of fatalities per incident and the probability of reporting. The right panel displays the probability of an incident being captured by at least one list over time. In both panels, the vertical axis represents the reporting probability. Lines denote posterior medians and shaded areas indicate 95% credible intervals.
  • Figure S1: Prior sensitivity analysis. For each of 108 alternative prior specifications, estimated posterior median, 2.5% quantile and 97.5% quantile are shown. Horizontal lines indicate estimates under the baseline prior specification.
  • Figure S2: Monte Carlo-based estimates of mortality rates. For top panels, interception probability is either assumed uniform or arising from a calibrated Beta distribution; see text for details. Bottom panel shows estimates based on a grid of fixed values for $\rho$. Point estimates refer to the posterior median. Shaded areas are 2.5% and 97.5% quantiles.
  • ...and 6 more figures